Use Dash to Build to Web Apps on FHIR Data

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Python Connector for FHIR, the pandas module, and the Dash framework, you can build FHIR-connected web applications for FHIR data. This article shows how to connect to FHIR with the CData Connector and use pandas and Dash to build a simple web app for visualizing FHIR data.

With built-in, optimized data processing, the CData Python Connector offers unmatched performance for interacting with live FHIR data in Python. When you issue complex SQL queries from FHIR, the driver pushes supported SQL operations, like filters and aggregations, directly to FHIR and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to FHIR Data

Connecting to FHIR data looks just like connecting to any relational data source. Create a connection string using the required connection properties. For this article, you will pass the connection string as a parameter to the create_engine function.

Set URL to the Service Base URL of the FHIR server. This is the address where the resources are defined in the FHIR server you would like to connect to. Set ConnectionType to a supported connection type. Set ContentType to the format of your documents. Set AuthScheme based on the authentication requirements for your FHIR server.

Generic, Azure-based, AWS-based, and Google-based FHIR server implementations are supported.

Sample Service Base URLs

• Generic: http://my_fhir_server/r4b/
• Azure: https://MY_AZURE_FHIR.azurehealthcareapis.com/
• AWS: https://healthlake.REGION.amazonaws.com/datastore/DATASTORE_ID/r4/
• Google: https://healthcare.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/datasets/DATASET_ID/fhirStores/FHIR_STORE_ID/fhir/

Generic FHIR Instances

The product supports connections to custom instances of FHIR. Authentication to custom FHIR servers is handled via OAuth (read more about OAuth in the Help documentation. Before you can connect to custom FHIR instances, you must set ConnectionType to Generic.

After installing the CData FHIR Connector, follow the procedure below to install the other required modules and start accessing FHIR through Python objects.

Install Required Modules

Use the pip utility to install the required modules and frameworks:

pip install pandas
pip install dash
pip install dash-daq

Visualize FHIR Data in Python

Once the required modules and frameworks are installed, we are ready to build our web app. Code snippets follow, but the full source code is available at the end of the article.

First, be sure to import the modules (including the CData Connector) with the following:

import os
import dash
import dash_core_components as dcc
import dash_html_components as html
import pandas as pd
import cdata.fhir as mod
import plotly.graph_objs as go

You can now connect with a connection string. Use the connect function for the CData FHIR Connector to create a connection for working with FHIR data.

cnxn = mod.connect("URL=http://test.fhir.org/r4b/;ConnectionType=Generic;ContentType=JSON;AuthScheme=None;")

Execute SQL to FHIR

Use the read_sql function from pandas to execute any SQL statement and store the result set in a DataFrame.

df = pd.read_sql("SELECT Id, [name-use] FROM Patient WHERE [address-city] = 'New York'", cnxn)

Configure the Web App

With the query results stored in a DataFrame, we can begin configuring the web app, assigning a name, stylesheet, and title.

app_name = 'dash-fhiredataplot'

external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']

app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
app.title = 'CData + Dash'

Configure the Layout

The next step is to create a bar graph based on our FHIR data and configure the app layout.

trace = go.Bar(x=df.Id, y=df.[name-use], name='Id')

app.layout = html.Div(children=[html.H1("CData Extension + Dash", style={'textAlign': 'center'}),
	dcc.Graph(
		id='example-graph',
		figure={
			'data': [trace],
			'layout':
			go.Layout(title='FHIR Patient Data', barmode='stack')
		})
], className="container")

Set the App to Run

With the connection, app, and layout configured, we are ready to run the app. The last lines of Python code follow.

if __name__ == '__main__':
    app.run_server(debug=True)

Now, use Python to run the web app and a browser to view the FHIR data.

python fhir-dash.py

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for FHIR to start building Python apps with connectivity to FHIR data. Reach out to our Support Team if you have any questions.

Full Source Code

import os
import dash
import dash_core_components as dcc
import dash_html_components as html
import pandas as pd
import cdata.fhir as mod
import plotly.graph_objs as go

cnxn = mod.connect("URL=http://test.fhir.org/r4b/;ConnectionType=Generic;ContentType=JSON;AuthScheme=None;")

df = pd.read_sql("SELECT Id, [name-use] FROM Patient WHERE [address-city] = 'New York'", cnxn)
app_name = 'dash-fhirdataplot'

external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']

app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
app.title = 'CData + Dash'
trace = go.Bar(x=df.Id, y=df.[name-use], name='Id')

app.layout = html.Div(children=[html.H1("CData Extension + Dash", style={'textAlign': 'center'}),
	dcc.Graph(
		id='example-graph',
		figure={
			'data': [trace],
			'layout':
			go.Layout(title='FHIR Patient Data', barmode='stack')
		})
], className="container")

if __name__ == '__main__':
    app.run_server(debug=True)